Job description: GCP BigData Engineering Experience level - 7 years total in Data Engineering; 4 years on GCP with production systems. Top 3 Must‑Have Skillsets GCP BigData Engineering (BigQuery Dataform Pub/Sub) Expert in designing and optimising BigQuery schemas, partitioning/clustering, cost/performance tuning, query optimisation, and policy tag integration. Building streaming and batch pipelines using Apache Beam/Dataflow and Pub/Sub with exactly-once semantics, backpressure handling, and replay strategies. Strong experience with Dataform (or similar) for SQL-based transformations, dependency graphs, unit tests, and multi-environment deployments. Python Orchestration (Airflow/Cloud Composer) Production-grade Python for ETL/ELT, distributed processing, robust error handling, and testable modular design. Designing resilient Airflow DAGs on Cloud Composer: dependency management, retries, SLAs, sensors, service accounts, and secrets. Monitoring, alerting, and Cloud Logging/Stackdriver integration for end-to-end pipeline observability. Data Security & Governance on GCP Hands-on with Dataplex (asset management, data quality, lineage), BigQuery policy tags, Cloud IAM (least privilege, fine-grained access), KMS (key rotation, envelope encryption), and audit trails via Cloud Logging. Practical experience implementing PII controls (data masking, tokenisation, attribute-based access control) and privacy-by-design in pipelines. Good‑to‑Have Skillsets · Cloud Run & APIs : Building stateless microservices for data access/serving layers, implementing REST/gRPC endpoints, authentication/authorisation, rate limiting. · Data Modelling : Telecom-centric event models (e.g., CDRs, network telemetry, session/flow data), star/snowflake schemas, and lakehouse best practices. · Performance Engineering : BigQuery slot management, materialised views, BI Engine, partition pruning, cache strategies. · Secure Source Manager (CI/CD) : Pipeline-as-code, automated tests, artifact versioning, environment promotion, canary releases, and GitOps patterns. · Infrastructure as Code : Terraform/Deployment Manager for reproducible environments, IAM bindings, service accounts, KMS config, Composer environments. · Data Quality & Testing : Great Expectations/Deequ-like checks, schema contracts, anomaly detection, and automated data validations in CI/CD. · Streaming Patterns : Exactly-once delivery, idempotent sinks, watermarking, late data handling, windowing strategies. · Observability & SRE Practices : Metrics, logs, traces, runbooks, SLIs/SLOs for data platforms, major incident response to support DevOps. · Cost Governance : BigQuery cost controls, slot commitments/reservations, workload management, storage lifecycle policies. · Domain Knowledge (Mobile Networks) : Familiarity with 3G/4G/5G network data, OSS/BSS integrations, network KPIs, and typical analytics use cases Experience Level · 7 years total in data engineering; 4 years on GCP with production systems · Evidence of impact: o Led end-to-end delivery of large-scale pipelines (batch streaming) with strict PII governance. o Owned performance/cost optimisation initiatives in BigQuery/Dataflow at scale. o Implemented CI/CD for data workflows (Secure Source Manager) including automated tests and environment promotion. o Drove operational excellence (SLAs, incident management, RTO/RPO awareness, DR patterns). · Soft skills: Technical leadership, code reviews, mentoring, clear documentation, cross-functional collaboration with Network/Analytics teams, and a bias for automation & reliability.
